Intra-Chunk Dependency Annotation : Expanding Hindi Inter-Chunk Annotated Treebank Prudhvi Kosaraju, Bharat Ram Ambati, Samar Husain Dipti Misra Sharma, Rajeev Sangal Language Technologies Research Centre IIIT Hyderabad

Treebank Linguistic resources in which each sentence has – Parse tree – morphological, syntactic and lexical information marked explicitly Some treebanks – Penn Treebank (Marcus et al., 1993) for English – Prague Dependency Treebank (Hajicova, 1998) for Czech. For Indian Languages – Lack of such treebank been a major bottleneck for advance research and development of NLP tools and applications

Treebank creation Annotated manually or semi-automatically Manual creation – Annotators has to follow prescribed guidelines – Costly process in terms of both money & time Semi-automatic creation – Running of tools or parsers – Manual correction of Errors Note: An accurate annotating parser/tool saves cost and time for both the annotation as well as the validation task

Hindi Treebank Multi-layered and multi-representational treebank having – Dependency relations – Verb arguments (PropBank, Palmer et al., 2005) – Phase structure Dependency treebank has information at – morpho-syntactic (morphological,part-of-speech (POS) and chunk) level – syntactico-semnatic (dependency) level

Hindi Dependency Treebank Manual annotation has been done at – Part_of_speech level – Chunk level – Morph level – Inter-chunk dependency level

Inter-chunk annotated sentence Sentence1: नीली किताब गिर गई niilii kitaab gir gaii ‘blue’ ‘book’ ‘fall’ ’go-perf’ The blue book fell down Figure 1: SSF Representation Figure 2: Inter-chunk dependency tree of sentence 1

Intra-chunk dependencies Intra-chunk dependencies left unannotated since – Identification of intra-chunk dependencies are quite deterministic – Can be automatically annotated with high degree of accuracy Marking intra-chunk dependencies on inter- chunk dependency annotated trees results expansion of the later Automatic conversion to phase structure depends upon the expanded version of the treebank Hence, a High quality intra-chunk dependency annotator/parser is required

Fig 3: SSF representation of complete dependency tree Fig 4: complete dependency tree of Sentence 1

Intra-chunk dependency annotation Guidelines Tags can be classified into – Normal dependencies nmod__adj, jjmod__intf etc – Local word group dependencies (lwg) lwg__psp, lwg__vaux, lwg__neg etc – Linking local word group dependencies lwg__cont etc Total of 12 tags were used for experiments

nmod__adj Various types of adjectival modifications are shown using this label. An adjective modifying a head noun is one such instance. The label also incorporates various other modifications such as a demonstrative or a quantifier modifying a noun Chunk: नीली किताब NP ((niilii_JJ kitaab_NN)) ‘blue ‘ ‘book’ niilii nmod__adj kitaab

lwg__psp Used to attach post-positions/ auxiliaries associated with the noun or a verb. ‘lwg’ in the label name stands for local word grouping and associates all the postpositions with the head noun C hunk: अभिषेक ने NP((abhishek_NNP ne_PSP)) ’abhishek’ ’ERG ’ abhishek lwg__psp ne

lwg__cont To show that a group of lexical items inside a chunk together perform certain function In such cases, we do not commit on the dependencies between these elements We see this with complex post-positions associated with a noun/verb or with the auxiliaries of a verb ‘cont’ stands for continue Chunk: जा सकता है VGF((jaa_VM sakataa_VAUX hai_VAUX)) ‘go’ ‘can’ ‘be-pres’ jaa lwg__vaux sakataa lwg__cont hai

Intra-chunk dependnecy annotator/parser Built a robust intra-chunk dependency parser for Hindi – Rule based Approach – Statistical Approach – Hybrid Approach (using heuristic based post- processing component on top of statistical approach) The rule based tool can easily adaptable to other languages as well

Rule based intra-chunk dependency annotator Identifies modifier-modified (parent-child) relationship inside a chunk Rules provided in a fixed rule template Heads in each chunk determined by head computation module All information present in the SSF can be captured through the rule template

Rule template We capture the rules in form of constraints applicable at Chunk Label Parent Constraints Child Constraints Contextual Constraints Table 1 : Rule template Chunk NameParent Constraints Child Constraints Contextual ConstraintsDep. Relation NPPOS == NNPOS == JJposn(parent) > posn(child); nmod__adj

Statistical approach : Sub-tree parsing using Malt parser Malt parser(Nivre et al., 2007), transition based dependency parser is best suited for identifying short range dependencies (Nivre, 2003) Each chunk is separated and called sub-tree Data is divided into training (192 sentences), development(64) and testing(64) We followed the strategies used in kosaraju et.al,2010 – Feature pool – Pruning features using forward selector

Results (on gold data) Table 2 : Data Statistics Table 3: Rule based accuracies Table 4: Statistical approach showing baseline, POS and best templates No:of Sentences Training192 Development64 Testing64 LAS97.89 UAS98.50 LS98.38 BaselinePOS -templateBest template LAS UAS LS

Data Statistics Table 2 : Data Statistics No:of Sentences Training192 Development64 Testing64

Results (on gold data) Table 3: Rule based accuracies Table 4: Statistical approach showing baseline, POS and best templates LAS97.89 UAS98.50 LS98.38 BaselinePOS -templateBest template LAS UAS LS

Hybrid approach Post processed the statistical approach output using the rules as heuristics Only those tag associated rules are considered for which recall in rule-based is greater than statistical approach – Pof__cn, nmod__adj,rsym Table 5: All methods parsing accuracies ApproachLASUASLS Rule-based Statistical Hybrid

Special Cases ‘Chunks are self contained units. Intra-chunk dependencies are chunk internal and do not span outside a chunk.’ The above is the basis for neat division of inter- chunk and intra-chunk parsing However, there are two cases this constraint does not hold. – In these two cases a chunk internal element that is not the head of the chunk has a relation with a lexical item outside its chunk Hence, these relations are to be handled seperately

Special cases rsym__EOS (End of Sentence marker): – Occurs in the last chunk, Attached to head of the sentence lwg__psp : – According to guidelines, psp attaches to head of the chunk with lwg__psp – However, if the right most child of a CCP (conjunction chunk) is a nominal (NP or VGNN), one needs to attach the PSP of this nominal child to the head of the CCP during expansion – If there are multiple PSP, then first PSP gets a lwg__psp and second lwg__cont

Special case (lwg__psp) NP(raama_NNP) CCP(aur_CC) NP(siitaa_NNP ne_PSP) ‘ram’ ‘and’ ‘sita’ ‘ERG’ aur ccof ccof lwg__psp raama ne sita Fig 5: Expanded sub-tree with PSP connected with CC

Conclusion Described annotation guidelines for marking intra-chunk dependency relations Approaches: 1.Rule based 2. Statistical 3. Hybrid (using 1&2) By error analysis the outputs, only certain tags are not being marked correctly. This is good news because then one can make very targeted manual corrections after the automatic tool is run

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